Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/agentsope/skillalchemy/agentsop-module-shape-selectionnpx skills add agentsope/SkillAlchemy --skill agentsop-module-shape-selectiongit clone --depth 1 https://github.com/agentsope/SkillAlchemyWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00174 | $0.04567 |
| Opus 5 | $0.00087 | $0.02284 |
| Sonnet 5 | $0.00035 | $0.00913 |
| Haiku 4.5 | $0.00017 | $0.00457 |
Grade A, and why
agentsop-module-shape-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
M2 — Module-Shape Selection (CoT / ReAct / PoT / Predict)
"Pick the lowest-power Module that works. Default to ChainOfThought." — DSPy docs [dspy.ai/learn/programming/modules/]
This overlay sharpens that line into a rubric: the default is not a law. The shape is a function of the task structure, and CoT is only one of four answers.
This is an enhancement overlay. It assumes the [[dspy]] library skill is loaded
(it provides dspy.Predict, dspy.ChainOfThought, dspy.ReAct,
dspy.ProgramOfThought APIs and install). This file adds only the decision the lib
skill leaves implicit. Cross-link: [[dspy]], and the optimizer SOP [[agentsop-dspy]].
1. 何时激活 (When to activate)
Activate the instant you are about to add or wrap an LM-calling step:
| Trigger | Signal |
|---|---|
| New node | A LangGraph/CrewAI node body, or a forward() line, is about to call an LM |
New dspy.<Module>(Sig) |
You are typing dspy.ChainOfThought(...) on reflex — stop and run the rubric |
| Refactor | An existing Predict "feels weak" or a ChainOfThought "feels wasteful" |
| Pipeline growth | A multi-stage program adds a stage; each stage needs its own shape decision |
| Tool appears | A function/API/search/calculator is now available to the step |
Do NOT activate when:
- The work is a one-shot prompt — just call the LM; shape ceremony has no payoff.
- You are choosing the optimizer / teleprompter (MIPROv2, GEPA, BootstrapFewShot) — that is the [[agentsop-dspy]] workflow, a later stage. Shape comes first, optimizer second.
- The step is non-LM control flow (a
if, a DB read, a deterministic transform).
Shape selection is upstream of optimization. You pick the shape in Stage 1 (Programming) of the dspy SOP, before any metric or compile [dspy.ai/learn/].
2. 核心心智模型 (Core mental model)
Reasoning shape is chosen by task structure, not by defaulting to CoT.
The lib skill shows four modules side by side and a "Best Practices" note that says
"Start with Predict, add ChainOfThought if needed" [~/.claude/skills/dspy Best
Practices §1]. In practice that collapses into a CoT-everywhere reflex, because
"if needed" is never operationalized. This overlay operationalizes it.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 326 lines · 174 tokens per session scan A 7bab8946f2bb
agentsop-module-shape-selection is a skill published in the GitHub repository agentsope/SkillAlchemy (342 stars, last pushed 8d ago), licensed MIT. It adds 174 tokens to every session and 4,567 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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